This vignette is the fourth procuded during my internship in TIMC-IMAG lab (BCM team). It is divided into two parts. The first one is the visualization of transcription along with heatmap of selected_genes closests DMRs ( window = 100k). The second one offer a new physical gene partition to analyze methylation.
We first divided our genes into 6 bins (-2500,-1500,-500,TSS,+500,+1500,+2500) to overview methylation measures over it. Then, was proposed a second approach by dividing genes into biological regions such as exons, introns etc. The following work flow logically from it : downstream the TSS, the biological regions approach is conserved. Upstream the TSS, the gene is separated into 3 bins : -2500:-1500, -1500:-500 and -500:TSS, to refine our vizualisation.
Here are some outputs of a plot_binreg function. For clarity, the large source code is not given here and is available on github. The examples plots are the same as used in the DMRs vignette on purpose.
As you mate note, the closest region to the TSS is sometimes overlapping with the TSS closest bin. It can lead to index a probe in both. This overlap is on purpose since border_indexs of biological regions can be blurred, i thought it was smarter to risk some noise than determine for good the matter.
We first define a get_indexed_binreg indexing probes as shown in previous plots.
We can now repeat our usual pipeline to visualize obtained results. It is noticeable that Rsd heatmap ( relative squared error), defined as \(\frac{\sigma}{| \mu |} \in [0,+\infty[\), is not well really informative because of its limits properties which are hard to deal with in a algorithmic way.
feat_ind <- get_indexed_binreg()
map_binreg<-reduce_map(feat_ind,c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
means_per_regions_per_genes_per_patient_h<- reduce_rows(meth_normal,map_binreg, mean ,na.rm=T)
means_h <- subset_vals_per_bins(data = meth_normal,
values_per_patient = means_per_regions_per_genes_per_patient_h,
fun = mean,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
order_index <- order(apply(means_h,1,mean,na.rm=T))
meth_heatmap(means_h, main = "mean of means superup/healthy",order_index = order_index)
sd_per_regions_per_genes_per_patient_h <- reduce_rows(meth_normal,map_binreg, sd ,na.rm=T)
sds_h <- subset_vals_per_bins(data = meth_normal,
values_per_patient = sd_per_regions_per_genes_per_patient_h,
fun = sd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(sds_h, main = "mean of sd superup/healthy",order_index = order_index)
rsd_per_regions_per_genes_per_patient_h <- reduce_rows(meth_normal,map_binreg, rsd ,na.rm=T)
rsds_h <- subset_vals_per_bins(data = meth_normal,
values_per_patient = rsd_per_regions_per_genes_per_patient_h,
fun = rsd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(rsds_h, main = "mean of rsd superup/healthy",order_index = order_index)
means_per_regions_per_genes_per_patient_t <- reduce_rows(meth_tumoral,map_binreg, mean ,na.rm=T)
means_t <- subset_vals_per_bins(data = meth_tumoral,
values_per_patient = means_per_regions_per_genes_per_patient_t,
fun = mean,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(means_t, main = "mean of means superup/tumoral",order_index = order_index)
sd_per_regions_per_genes_per_patient_t <- reduce_rows(meth_tumoral,map_binreg, sd ,na.rm=T)
sds_t <- subset_vals_per_bins(data = meth_tumoral,
values_per_patient = sd_per_regions_per_genes_per_patient_t,
fun = sd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(sds_t, main = "mean of sd superup/tumoral",order_index = order_index)
rsd_per_regions_per_genes_per_patient_t <- reduce_rows(meth_tumoral,map_binreg, rsd ,na.rm=T)
rsds_t <- subset_vals_per_bins(data = meth_tumoral,
values_per_patient = rsd_per_regions_per_genes_per_patient_t,
fun = rsd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(rsds_t, main = "mean of rsd superup/tumoral",order_index = order_index)
boxplot_res(means_h,means_t)
boxplot_res(sds_h,sds_t)
boxplot_res(rsds_h,rsds_t)
means_per_regions_per_genes_per_patient_d<- reduce_rows(meth_diff,map_binreg, mean ,na.rm=T)
means_d <- subset_vals_per_bins(data = meth_diff,
values_per_patient = means_per_regions_per_genes_per_patient_d,
fun = mean,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(means_d, main = "mean of means superup/differential",order_index = order_index)
sd_per_regions_per_genes_per_patient_d <- reduce_rows(meth_diff,map_binreg, sd ,na.rm=T)
sds_d <- subset_vals_per_bins(data = meth_diff,
values_per_patient = sd_per_regions_per_genes_per_patient_d,
fun = sd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(sds_d, main = "mean of sd superup/differential",order_index = order_index)
rsd_per_regions_per_genes_per_patient_d <- reduce_rows(meth_diff,map_binreg, rsd ,na.rm=T)
rsds_d <- subset_vals_per_bins(data = meth_diff,
values_per_patient = rsd_per_regions_per_genes_per_patient_d,
fun = rsd,
binlist=c("bin1","bin2","bin3","INTER","UTR5","INTRON","CDS","UTR3"))
meth_heatmap(rsds_d, main = "mean of rsd superup/differential",order_index = order_index)
order_index <- order(means_h[,6])
meth_heatmap(means_h, main = "mean of means superup/healthy",order_index=order_index)
meth_heatmap(sds_h, main = "mean of sd superup/healthy", order_index=order_index)
meth_heatmap(rsds_h, main = "mean of rsd superup/healthy",order_index=order_index)
meth_heatmap(means_t, main = "mean of means superup/tumoral",order_index=order_index)
meth_heatmap(sds_t, main = "mean of sd superup/tumoral",order_index=order_index)
meth_heatmap(rsds_t, main = "mean of rsd superup/tumoral",order_index=order_index)
meth_heatmap(means_d, main = "mean of means superup/differential",order_index=order_index)
meth_heatmap(sds_d, main = "mean of sd superup/differential",order_index=order_index)
meth_heatmap(rsds_d, main = "mean of rsd superup/differential",order_index=order_index)
order_index <- order(means_h_d[,6])
meth_heatmap(means_h_d, main = "mean of means superdown/healthy",order_index=order_index)
meth_heatmap(sd_h_d, main = "mean of sd superdown/healthy",order_index=order_index)
meth_heatmap(rsds_h_d, main = "mean of rsd superdown/healthy",order_index=order_index)
meth_heatmap(means_t_d, main = "mean of means superdown/tumoral",order_index=order_index)
meth_heatmap(sd_t_d, main = "mean of sd superdown/tumoral",order_index=order_index)
meth_heatmap(rsds_t_d, main = "mean of rsd superdown/tumoral",order_index=order_index)
meth_heatmap(means_d_d, main = "mean of means superdown/diff",order_index=order_index)
meth_heatmap(sd_d_d, main = "mean of sd superdown/diff",order_index=order_index)
meth_heatmap(rsds_d_d, main = "mean of rsd superdown/diff",order_index=order_index)
order_index <- order(means_h_c[,6])
meth_heatmap(means_h_c, main = "mean of means supercons/healthy",order_index=order_index)
meth_heatmap(sd_h_c, main = "mean of sd supercons/healthy",order_index=order_index)
meth_heatmap(rsds_h_c, main = "mean of rsd supercons/healthy",order_index=order_index)
meth_heatmap(means_t_c, main = "mean of means supercons/tumoral",order_index=order_index)
meth_heatmap(sd_t_c, main = "mean of sd supercons/tumoral",order_index=order_index)
meth_heatmap(rsds_t_c, main = "mean of rsd supercons/tumoral",order_index=order_index)
meth_heatmap(means_d_c, main = "mean of means supercons/diff",order_index=order_index)
meth_heatmap(sd_d_c, main = "mean of sd supercons/diff",order_index=order_index)
meth_heatmap(rsds_d_c, main = "mean of rsd supercons/diff",order_index=order_index)
order_index <- order(means_h[,3])
meth_heatmap(means_h, main = "mean of means superup/healthy",order_index=order_index)
meth_heatmap(sds_h, main = "mean of sd superup/healthy", order_index=order_index)
meth_heatmap(rsds_h, main = "mean of rsd superup/healthy",order_index=order_index)
meth_heatmap(means_t, main = "mean of means superup/tumoral",order_index=order_index)
meth_heatmap(sds_t, main = "mean of sd superup/tumoral",order_index=order_index)
meth_heatmap(rsds_t, main = "mean of rsd superup/tumoral",order_index=order_index)
meth_heatmap(means_d, main = "mean of means superup/differential",order_index=order_index)
meth_heatmap(sds_d, main = "mean of sd superup/differential",order_index=order_index)
meth_heatmap(rsds_d, main = "mean of rsd superup/differential",order_index=order_index)
order_index <- order(means_h_d[,3])
meth_heatmap(means_h_d, main = "mean of means superdown/healthy",order_index=order_index)
meth_heatmap(sd_h_d, main = "mean of sd superdown/healthy",order_index=order_index)
meth_heatmap(rsds_h_d, main = "mean of rsd superdown/healthy",order_index=order_index)
meth_heatmap(means_t_d, main = "mean of means superdown/tumoral",order_index=order_index)
meth_heatmap(sd_t_d, main = "mean of sd superdown/tumoral",order_index=order_index)
meth_heatmap(rsds_t_d, main = "mean of rsd superdown/tumoral",order_index=order_index)
meth_heatmap(means_d_d, main = "mean of means superdown/diff",order_index=order_index)
meth_heatmap(sd_d_d, main = "mean of sd superdown/diff",order_index=order_index)
meth_heatmap(rsds_d_d, main = "mean of rsd superdown/diff",order_index=order_index)
order_index <- order(means_h_c[,3])
meth_heatmap(means_h_c, main = "mean of means supercons/healthy",order_index=order_index)
meth_heatmap(sd_h_c, main = "mean of sd supercons/healthy",order_index=order_index)
meth_heatmap(rsds_h_c, main = "mean of rsd supercons/healthy",order_index=order_index)
meth_heatmap(means_t_c, main = "mean of means supercons/tumoral",order_index=order_index)
meth_heatmap(sd_t_c, main = "mean of sd supercons/tumoral",order_index=order_index)
meth_heatmap(rsds_t_c, main = "mean of rsd supercons/tumoral",order_index=order_index)
meth_heatmap(means_d_c, main = "mean of means supercons/diff",order_index=order_index)
meth_heatmap(sd_d_c, main = "mean of sd supercons/diff",order_index=order_index)
meth_heatmap(rsds_d_c, main = "mean of rsd supercons/diff",order_index=order_index)